AI Agent for Product Managers
You write a spec. You hand it to engineering. Then you spend the next sprint in clarification meetings — three tickets asking what "handle the edge case" actually means, a Slack thread about which auth library you had in mind, a call to explain scope that wasn't in the doc.
The spec looked fine. The problem was that it didn't know the codebase.
Tekk is an AI agent for product managers that reads your repository before generating a single line of requirements. The output isn't a template-filled document — it's a technically grounded PRD with subtasks, acceptance criteria, file references, and explicit scope boundaries, streamed directly into an editable spec your team works from.
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How Tekk Works as an AI Agent for Product Managers
Most AI tools for product managers start with a template. They ask you questions, fill in the blanks, and produce a well-formatted requirements doc you export to Notion or Confluence. That doc still requires your engineers to figure out where the new feature fits — which files, which dependencies, what's already built and what isn't. You've outsourced the writing. You haven't solved the handoff.
Tekk starts somewhere different: your codebase. Before generating anything, the agent runs semantic search across your repository, browses directory structure, profiles your languages and frameworks, and maps the dependencies relevant to what you're building. It reads your code the way a senior engineer would before taking on a ticket.
The PRD it produces is grounded in what's actually there. Subtasks are behavioral slices — "user can now do X" — with acceptance criteria and specific file references. The "Not Building" section defines scope boundaries before anyone writes code. Assumptions include risk levels. Validation scenarios are concrete and end-to-end.
The spec isn't something you export. It streams into a live editable document inside your planning workspace — connected to the task, the codebase context, and the conversation that produced it. No copy-pasting from chat threads. No re-explaining context next session.
Key Benefits
Technically grounded PRDs, not template fills Tekk generates specs from your actual repository. Subtasks reference real files and existing code patterns. Engineering doesn't have to translate abstract requirements into a real codebase — the spec already did that work.
Subtasks with acceptance criteria engineering can act on Every plan includes end-to-end behavioral subtasks with acceptance criteria, file references, and dependencies. The level of precision AI coding agents (Cursor, Claude Code, Codex) need to execute without back-and-forth.
Explicit scope boundaries before a line of code is written Every spec has a "Not Building" section. What's in, what's out — defined upfront. This is the structural discipline that template-based PRDs skip, and it's where scope creep starts.
A living document, not a chat message The spec lives in your workspace as an editable rich-text document. Revise it inline. Link it to the task. It persists across sessions, connected to the codebase context that generated it.
How It Works
Step 1: Connect your repository Link your GitHub, GitLab, or Bitbucket repo. Tekk profiles it — languages, frameworks, services, dependencies — before your first session.
Step 2: Describe the feature Tell Tekk what you're building. One sentence is enough to start. The agent asks 3–6 questions grounded in what it found in the code — no generic questions about things the codebase already answers.
Step 3: Review architecture options When there's more than one viable approach, Tekk presents 2–3 architecturally distinct options with honest tradeoffs. You pick one, or push back, or ask for more.
Step 4: Get the spec The agent writes the full PRD in real time, streamed into a BlockNote editor as your working document. TL;DR, scope boundaries, subtasks with acceptance criteria and file references, assumptions with risk levels, validation scenarios.
Step 5: Hand it off Edit the spec inline if needed. Send it to engineering — or trigger your AI coding agent. The spec has what it needs to execute.
Who This Is For
You're a PM at a startup or a small team. You own requirements and you work directly with engineers — there's no dedicated architect in the middle translating your specs. You write PRDs, engineers implement them, and somewhere between those two steps, details get lost.
You've tried ChatPRD. It's fast and produces solid-looking documents. But your engineers still come back with questions, because the spec was written in the abstract. It didn't know your auth setup, your current data model, or which third-party library you're already using. ChatPRD gave you a polished template. It didn't give you a spec engineering could execute without you.
You might also use Linear's AI features or Notion AI for requirements. Same problem — these tools work on top of your ideas, not inside your codebase. The gap between "what the spec says" and "what the code needs" doesn't close.
If you're working with AI coding agents — Cursor, Claude Code, Codex — this gap becomes acute. These agents execute from prompts. A vague paragraph produces a flailing agent that builds the wrong thing and needs three rounds of correction. What they need is the kind of spec Tekk produces: subtasks with acceptance criteria, file targets, and scope boundaries. Not a template. Not a paragraph.
Tekk is also useful if you're non-technical and you know it. Not in a condescending way — just that you're speccing features in a codebase you don't spend every day reading. Tekk bridges that. You describe the problem in plain language. The agent reads the code and generates a spec that reflects what's actually there.
Who it's not for: PMs who need enterprise approval workflows, Jira-style governance, or custom backlog management at scale. Tekk is opinionated and lightweight — no process theater.
What Can an AI Agent Do for Product Managers?
An AI agent for product managers is a system that handles part of the PM workflow autonomously — drafting requirements, analyzing feedback, generating specs, surfacing tradeoffs — rather than waiting for a human to prompt each step.
The category covers a wide range: tools like ChatPRD operate as AI writing assistants (you provide context, they produce structured output); platforms like BuildBetter.ai focus on feedback synthesis and discovery; broader AI agents work across research, documentation, and communication in parallel.
The shift that matters for 2025–2026: AI coding agents have made the quality of PM specs a direct productivity variable. When Cursor or Codex executes from a well-structured spec, it ships working code. When it executes from an ambiguous paragraph, it generates rework. Product managers who write for AI coding agents need output at a different level of precision than PMs who write for human engineers alone.
The gap that no existing PM AI tool has closed: codebase awareness. Templates produce well-formatted requirements. What's missing is requirements that reflect the specific architecture, dependencies, and file structure of the product being built. That's where the clarification meetings come from — and it's the specific problem Tekk addresses.
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If your sprints keep opening with clarification meetings, the spec is the problem — not your engineers. Connect your repo, describe one feature, and see what a codebase-grounded PRD actually looks like.
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